Vmware, Inc.

Rajat Garg

Chandrashekhar Jha

Yash Bhatnagar

Amit Kumar

Kruti Erraguntala

Vishal I Gupta

Ritesh Ranjan Jha

Rachil Chandran

Sivaraj M

Mageshwaran Rajendran

Naveen Mudnal

Gerin Jacob

Santoshkumar Kavadimatti

A disclosed example to determine a migration recommendation of a service between geographic regions includes: a graph generator to generate an interaction graph, the interaction graph including first and second nodes and an edge therebetween, the first node representative of a first service in a first geographic region, the second node representative of a second service in a second geographic region, and the edge representative of a network path of interactions between the first and second services; a weighing engine to determine a weight value of the edge between the first and second services based on a count of network interactions between the first and second services and a real-time latency between the first and second services; and a recommendation engine to generate a migration recommendation to migrate the first service to the second geographic region based on the weight value of the edge.

Methods and apparatus to generate migration recommendations to migrate services between geographic regions

Methods, apparatus, systems and articles of manufacture are disclosed to detect drift in a hybrid cloud environment. An example apparatus to detect drift in a hybrid cloud environment includes a configuration model determiner to, after deployment of a blueprint in the hybrid cloud environment, generate a first model including first relationships of a first plurality of resources corresponding to the blueprint, the blueprint including a plurality of properties in which at least one of the plurality of properties is agnostic of type of cloud, an inventor model determiner to generate a second model including second relationships of a second plurality of resources as deployed in the hybrid cloud environment based on the blueprint, and a drift determiner to determine a drift value based on the first relationships and the second relationships, the drift value representative of a difference between the first relationships and the second relationships.

Methods and apparatus to detect drift in a hybrid cloud environment

Disclosed are various embodiments for automating the prediction of workload tenures in datacenter environments. In some embodiments, parameters are identified for a plurality of workloads of a software defined data center. A machine learning model is trained to determine a predicted tenure based on parameters of the workloads. A workload for the software defined data center is configured to include at least one workload parameter. The workload is processed using the trained machine learning model to determine the predicted tenure. An input to the machine learning model includes the at least one workload parameter.

Workload tenure prediction for capacity planning

Examples described herein include systems and methods for optimizing the number of servers in a cluster. In one example, a number of application servers, a number of backend servers, and a first disk throughput of a backend server to be included in the cluster are determined. The first disk throughput is determined based on the storage capacity of the backend server and a first round trip time. Example systems and method can also include validating the number of application servers based on a cluster throughput and one of a network interface card bandwidth of an application server to be included in the cluster and a load bearing capacity of the application server. The systems and methods can further include determining a second disk throughput of the backend server and increasing the number of backend servers if the second disk throughput is less than the second disk throughput.

Systems and methods for optimizing the number of servers in a cluster

Various aspects are disclosed for optimization of dependent systems for serverless frameworks. In some examples, a load test executes instances of a function on a dependent system to generate datapoints. The datapoints are organized, using a clustering algorithm, into an acceptable group and at least one unacceptable group. A maximum number of concurrent instances of the function is determined based on a number of instances specified by at least one datapoint selected from the acceptable group. A live workload is performed on the dependent system. The live workload includes instances of the function that are assigned to the dependent system according to the maximum number of concurrent instances.

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Bengaluru, India

Rajat Garg